Chapter8.qmd

---
title: "Chapter 8"
subtitle: "Annotations"
author: "Aditya Dahiya"
date: "2023-12-17"
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---

::: callout-warning
This Chapter has no exercises. So, we explore annotations and packages using [Holiday Episodes data](https://github.com/rfordatascience/tidytuesday/blob/master/data/2023/2023-12-19/readme.md) from [#TidyTuesday](https://github.com/rfordatascience/tidytuesday/tree/master) All code is annotated to explain the steps.
:::

Loading Libraries and the Data-Set: ---

```{r}
#| label: setup
#| message: false
#| error: false
#| warning: false
#| code-fold: false

library(tidyverse)         # data wrangling and ggplot2
library(summarytools)      # EDA
library(gt)                # Tables
library(ggthemes)          # Themes
library(ggtext)            # Text, Annotations and Markdown
library(magick)            # Images

tuesdata <- tidytuesdayR::tt_load('2023-12-19')
holep <- tuesdata$holiday_episodes
holep_genres <- tuesdata$holiday_episode_genres
rm(tuesdata)
```

A look at 5 rows of the complete data set: ---

```{r}
# Pipe the 'holep' dataframe through a series of operations using the magrittr pipe operator %>%
holep |> 
  
  # Select the first 5 rows for top 5 highest votes received TV episodes
  slice_max(num_votes, n = 5) |> 
  
  # Create a gt table
  gt() |> 
  
  # Label columns using janitor::make_clean_names function
  cols_label_with(fn = ~ janitor::make_clean_names(., case = "title")) |> 
 
   # Apply styling to the table cells to make the text small
  tab_style(
    style = cell_text(size = "small"),
    locations = cells_body()
  ) |> 
  
  # Apply the gt theme from gtExtras package
  gtExtras::gt_theme_nytimes()

```

## **8.1 Plot and axis titles**

```{r}
#| fig-cap: "Scatterplot of TV Episodes Ratings vs. Runtime - demonstrating 'labs' of ggplot2 - markdown elements"
#| label: fig-8-1

# Pipe the 'holep' dataframe through a series of operations using the magrittr pipe operator %>%
holep |>
  
  # Select the top 200 rows based on the 'num_votes' column
  slice_max(order_by = num_votes, n = 200) |>
  
  # Arrange the data in descending order of 'num_votes'
 
  arrange(desc(num_votes)) |>
  
  # Add new columns: 'id' (row number) and 
  # 'primary_title' (conditional labeling)
  mutate(
    id = row_number(),
    primary_title = if_else(id <= 20,
      primary_title,
      NA
    )
  ) |>
  
  # Create a ggplot scatterplot
  ggplot(aes(x = runtime_minutes, y = average_rating)) +
  geom_smooth(method = "lm", col = "lightblue", se = FALSE) +
  geom_jitter(aes(size = num_votes), alpha = 0.2) +
  geom_text(aes(label = primary_title), 
            check_overlap = TRUE, 
            col = "black") +
 
   # Customize axis scales and size scale
  scale_x_continuous(breaks = c(0, 15, 30, 45, 60, 90, 120), 
                     limits = c(0, 120)) +
  scale_size_continuous(range = c(1, 10), 
                        labels = scales::label_number_si(), 
                        trans = "sqrt") +
  
  # Set themes for the plot
  cowplot::theme_half_open() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    axis.title.x = element_markdown(hjust = 1),
    axis.title.y = element_markdown(hjust = 1),
    plot.title = element_markdown(face = "plain"),
    legend.title = element_markdown(),
    axis.line = element_line(arrow = arrow(angle = 15, 
                                           length = unit(4, "mm")))
  ) +
 
   # Add labels and annotations
  labs(
    x = "Run-time *(in min.)*",
    y = "Average Rating",
    size = "Number of Votes (on **IMDb**)",
    title = "**IMDb** Holiday TV Show Episodes: Ratings _vs._ Run-time",
    subtitle = "A Scatterplot of the top-200 episodes (by number of votes on IMBDb) of holiday TV shows,\nwith top 20 labelled with names of episodes"
  ) +
  
  annotate(
    geom = "text",
    label = quote(y == a.x + b),
    x = 90, y = 6,
    col = "blue",
    fontface = "italic"
  )

```

## **8.2 Text labels**

```{r}
#| fig-asp: 1
#| label: fig-8-2
#| fig-cap: "Demonstrating the use of Text Labels in place of points in a scatterplot"

# Pipe the 'holep' dataframe through a series of operations using the magrittr pipe operator %>%
holep |>
  
  # Group the data by 'parent_primary_title'
  group_by(parent_primary_title) |>
 
   # Summarize the data: count of episodes, mean votes, mean ratings, and concatenate unique genres
  summarise(
    n = n(),
    votes = mean(parent_num_votes),
    ratings = mean(parent_average_rating),
    genre = paste(unique(genres), collapse = ",")
  ) |>
 
  # Select the top 40 rows based on 'votes'
  slice_max(order_by = votes, n = 40) |>
  
  # Arrange the data in descending order of 'votes'
  arrange(desc(votes)) |>
  
  # Add a new column 'gen_col' based on genre classification
  mutate(gen_col = case_when(
    str_detect(genre, "Comedy") ~ "Comedy",
    str_detect(genre, "Horror") ~ "Horror",
    str_detect(genre, "Action") ~ "Action",
    str_detect(genre, "Drama") ~ "Drama",
    .default = "Others"
  )) |>
  
  # Create a ggplot scatterplot
  ggplot(aes(x = votes, y = ratings, size = n, label = parent_primary_title, color = gen_col)) +
  geom_text(check_overlap = TRUE, hjust = "inward") +
  
  # Customize labels, titles, and scales
  labs(
    x = "Number of votes (on IMDb) for the TV Series",
    y = "Average Rating of episodes",
    size = "Number of Holiday Season episodes",
    color = "Genre",
    title = "Most popular TV Series with at least 1 holiday episode",
    subtitle = "A scatter-plot replaced by Text Labels"
  ) +
  scale_size_continuous(range = c(4, 7)) +
  scale_x_continuous(labels = scales::label_number_si(), 
                     limits = c(0, 1100000)) +
  scale_color_manual(values = c("blue", "red", "darkgreen", "purple")) +
  
  # Set themes for the plot
  theme_classic() +
  theme(
    legend.position = "bottom",
    legend.box = "vertical",
    legend.margin = margin(0, 0, 0, 0),
    legend.spacing = unit(0, "pt"),
    axis.line.x = element_line(arrow = arrow(angle = 15))
  )

```

## **8.3 Building custom annotations**

```{r}
#| label: fig-8-3
#| fig-cap: "Text Annotations within a plot's panel area"
#| fig-width: 10
#| fig-asp: 0.8

# IMDb logo image for annotation in the plot
img <- image_read("https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/171_Imdb_logo_logos-512.png")

# Extract the top 10 TV series with at least 1 holiday episode based on IMDb votes
tv10 <- holep |>
  group_by(parent_tconst, parent_primary_title) |>
  summarise(
    start = mean(parent_start_year, na.rm = TRUE),
    end = mean(parent_end_year, na.rm = TRUE),
    votes = mean(parent_num_votes, na.rm = TRUE),
    runtime = mean(parent_runtime_minutes, na.rm = TRUE),
    rating = mean(parent_average_rating, na.rm = TRUE),
    num_episodes = n()
  ) |>
  ungroup() |>
  mutate(years = end - start) |>
  drop_na() |>
  slice_max(order_by = votes, n = 10)

# Filter the 'holep' dataframe to see only the holiday episodes of the top 10 series
holep |>
  filter(parent_primary_title %in% (tv10 |> pull(parent_primary_title))) |>
  mutate(
    parent_primary_title =
      fct(parent_primary_title,
        levels = (tv10 |> pull(parent_primary_title))
      )
  ) |>
  
  # Create a ggplot scatterplot
  ggplot(aes(x = year, y = fct_rev(parent_primary_title))) +
  ggrepel::geom_text_repel(aes(label = primary_title),
                           vjust = +1) +
  geom_segment(
    data = tv10,
    aes(x = start, xend = end, y = parent_primary_title, yend = parent_primary_title),
    alpha = 0.3, lineend = "round", lwd = 3
  ) +
  geom_point(
    aes(color = average_rating, size = num_votes),
    alpha = 0.7
  ) +
  
  # Customize labels, titles, and scales
  labs(
    x = NULL, y = NULL,
    title = "Holiday Episodes of IMDb's top 10 TV Series",
    subtitle = "Size of dot indicates number of votes, Color indicates Average Rating on IMDb",
    colour = "Average Rating\n(IMDb)",
    size = "Number of Votes (IMDb)"
  ) +
  scale_color_gradient(low = "pink", high = "purple") +
  scale_size_continuous(range = c(2, 8)) +
  theme_minimal() +
  theme(
    legend.position = "bottom", 
    plot.title.position = "plot"
  ) +
  annotate(
    geom = "label",
    x = 1993,
    y = 1.5,
    label = "Source: IMDb Non-Commercial Datasets",
    fontface = "italic",
    hjust = 0,
    fill = "#f2c522"
  ) +
  annotation_custom(
    grob = grid::rasterGrob(img),
    xmin = 1993,
    xmax = 1998,
    ymin = 2,
    ymax = 5
  )

```

### Building a Visualization with Image annotations on the *y-axis*

An attempt to make a nice visualization with annotations for [#TidyTuesday](https://github.com/rfordatascience/tidytuesday/tree/master): ---

```{r}
#| eval: false


#==============================================================================#
# Libraries --------------------------------------------------------------------
#==============================================================================#
library(tidyverse)      # Data Wrangling and Plotting
library(here)           # Files location and loading
library(summarytools)   # Exploratory Data Analysis
library(colorfindr)     # To get colour palettes for the Viz
library(showtext)       # Using Fonts More Easily in R Graphs
library(ggimage)        # Using Images in ggplot2
library(fontawesome)    # Social Media icons
library(ggtext)         # Markdown Text in ggplot2
library(patchwork)      # For compiling plots
library(figpatch)       # Images in patchwork
library(magick)         # Work with Images and Logos
library(ggimage)        # Background Image
library(cropcircles)    # Crop Images
library(cowplot)        # Images on axis ticks

#==============================================================================#
# Data Load-in------------------------------------------------------------------
#==============================================================================#

tuesdata <- tidytuesdayR::tt_load('2023-12-19')
holep <- tuesdata$holiday_episodes
rm(tuesdata)

#==============================================================================#
# Data Wrangling----------------------------------------------------------------
#==============================================================================#
# Find Top 10 series of IMDb
tv10 <- holep |> 
  group_by(parent_tconst, parent_primary_title) |> 
  summarise(
    start = mean(parent_start_year, na.rm = TRUE),
    end = mean(parent_end_year, na.rm = TRUE),
    votes = mean(parent_num_votes, na.rm = TRUE),
    runtime = mean(parent_runtime_minutes, na.rm = TRUE),
    rating = mean(parent_average_rating, na.rm = TRUE), 
    num_episodes = n()
  ) |> 
  ungroup() |> 
  mutate(years = end - start) |> 
  drop_na() |> 
  slice_max(order_by = votes, n = 10)

# The Actual Data to be plotted
df <- holep |> 
  
  # See only the holiday episodes of top 10 series
  filter(
    parent_primary_title %in% (tv10 |> pull(parent_primary_title))
  ) |> 
  # An ordered factor to display TV Series Ranking wise in the plot
  mutate(
    parent_primary_title = 
      fct(parent_primary_title,
          levels = (tv10 |> pull(parent_primary_title))))

#==============================================================================#
# Options & Visualization Parameters--------------------------------------------
#==============================================================================#

# Load fonts
font_add_google("Ubuntu Condensed", 
                family = "title_font")       # Font for titles
font_add_google("Saira Extra Condensed", 
                family = "caption_font")     # Font for the caption
font_add_google("Exo", 
                family = "body_font")            # Font for plot text
showtext_auto()

# Creating Images for 10 Series Titles
# Image to extract
img <- ""

# Color Palette
library(MetBrewer)
MetBrewer::display_all()
mypal <- met.brewer("Tam")

# Define colours
low_col <- mypal[2]                   # Heat map: low colour
hi_col <- mypal[5]                    # Heat map: high colour
bg_col <- "white"                    # Background Colour
text_col <- mypal[8]                  # Colour for the text
text_hil <- mypal[7]                  # Colour for highlighted text

# Define Text Size
ts = 24                              # Text Size

# Caption stuff
sysfonts::font_add(family = "Font Awesome 6 Brands",
                   regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf"))
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
linkedin <- "&#xf08c"
linkedin_username <- "dr-aditya-dahiya-ias"
social_caption <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_col}'>{github_username}  </span> <span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_col}'>{xtwitter_username}</span> <span style='font-family:\"Font Awesome 6 Brands\";'>{linkedin};</span> <span style='color: {text_col}'>{linkedin_username}</span>")

# Add text to plot--------------------------------------------------------------
plot_title <- "Holiday Episodes of IMDb's top-10 TV Series"

subtitle_text <- "The Office had the most (6) holiday season episodes, while the highest rated episode is Dexter's Dex Takes a Holiday."
plot_subtitle <- paste(strwrap(subtitle_text, 150), collapse = "\n")

plot_caption <- paste0("**Data:** IMDb Non-Commercial Datasets. | ", "**Graphics:** ", social_caption)


#==============================================================================#
# Images for Y-Axis ------------------------------------------------------------
#==============================================================================#
url1 <- "https://www.tvstyleguide.com/wp-content/uploads/2017/05/the_vampire_diaries_logo-1.jpg"
url2 <- "https://resizing.flixster.com/-XZAfHZM39UwaGJIFWKAE8fS0ak=/v3/t/assets/p9974290_b_h8_ba.jpg"
url3 <- "https://cdn1.edgedatg.com/aws/v2/abc/ModernFamily/showimages/cae29355a2f177539897e6db1d9b0861/1600x900-Q90_cae29355a2f177539897e6db1d9b0861.jpg"
url4 <- "https://1000logos.net/wp-content/uploads/2017/07/emblem-Supernatural.jpg"
url5 <- "https://pics.filmaffinity.com/House_M_D_TV_Series-298794401-large.jpg"
url6 <- "https://cdn.britannica.com/63/247263-050-3ABF5622/promotional-still-The-Office-Steve-Carell.jpg"
url7 <- "https://m.media-amazon.com/images/M/MV5BNjg1MDQ5MjQ2N15BMl5BanBnXkFtZTYwNjI5NjA3._V1_FMjpg_UX1000_.jpg"
url8 <- "https://rukminim2.flixcart.com/image/850/1000/k0zlsi80/poster/f/p/y/medium-dexter-tv-series-poster-for-room-office-13-inch-x-19-inch-original-imafknhcvrnzxfwy.jpeg"
url9 <- "https://resizing.flixster.com/-XZAfHZM39UwaGJIFWKAE8fS0ak=/v3/t/assets/p185554_b_v9_bk.jpg"
url10 <- "https://m.media-amazon.com/images/M/MV5BNDVkYjU0MzctMWRmZi00NTkxLTgwZWEtOWVhYjZlYjllYmU4XkEyXkFqcGdeQXVyNTA4NzY1MzY@._V1_.jpg"

mk_logo <- function(url){
  image_read(url) |> 
  image_resize("x300") |> 
  circle_crop(border_size = 1, 
              border_colour = "black") |> 
  image_read()
  }

#==============================================================================#
# Data Visualization------------------------------------------------------------
#==============================================================================#

p <- df |> 
  ggplot(aes(x = year,
             y = fct_rev(parent_primary_title))) + 
  ggrepel::geom_text_repel(
    aes(label = primary_title),
    family = "body_font",
    col = mypal[7],
    size = 3, nudge_y = -0.5) +
  geom_segment(
    data = tv10,
    aes(x = start,
        xend = end,
        y = parent_primary_title,
        yend = parent_primary_title),
    alpha = 0.3,
    lineend = "round",
    lwd = 4, 
    col = mypal[7]
  ) +
  geom_point(
    aes(color = average_rating,
        size = num_votes),
    alpha = 0.96
  ) +
  scale_color_gradient(low = low_col, 
                        high = hi_col) +
  scale_size_continuous(range = c(4, 12),
                        labels = scales::label_number_si()) +
  scale_x_continuous(limits = c(1993, 2023),
                     breaks = seq(1995, 2020, 5),
                     expand = c(0, 0)) +
  theme_minimal() +
  theme(
    legend.position = "bottom"
  ) +
  labs(title = plot_title,
       caption = plot_caption,
       subtitle = NULL,
       x = NULL, y = NULL,
       color = "Episode Rating (IMDb)",
       size = "Number of Votes (IMDb)") +
  guides(size = guide_legend(override.aes = list(colour = text_hil)),
         alpha = "none") + 
  theme(
    plot.caption =  element_textbox(family = "caption_font",
                                    hjust = 0.5,
                                    colour = text_col,
                                    size = ts/2),
    plot.title   =     element_text(hjust = 0.5,
                                    size = 2*ts,
                                    family = "title_font",
                                    face = "bold",
                                    colour = text_col),
    plot.subtitle    = element_text(hjust = 0,
                                    size = ts/2,
                                    family = "body_font",
                                    colour = text_col),
    plot.background =  element_rect(fill = bg_col,
                                    color = bg_col,
                                    linewidth = 0),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major.y = element_line(linetype = 2),
    axis.text       =  element_text(hjust = 0.5,
                                    size = ts/2,
                                    family = "body_font",
                                    colour = text_col),
    legend.title = element_text(family = "body_font",
                                colour = text_col,
                                vjust = 0.5),
    legend.key.height = unit(2, "mm"),
    legend.text = element_text(family = "body_font",
                               colour = text_col),
    plot.title.position = "plot",
    plot.caption.position = "plot"
    )
scale_fac = 0.9

pimage <- axis_canvas(
  p, 
  axis = "y") + 
  draw_image(mk_logo(url1), y = 0.5, scale = scale_fac) +
  draw_image(mk_logo(url2), y = 1.5, scale = scale_fac) +
  draw_image(mk_logo(url3), y = 2.5, scale = scale_fac) +
  draw_image(mk_logo(url4), y = 3.5, scale = scale_fac) +
  draw_image(mk_logo(url5), y = 4.5, scale = scale_fac) +
  draw_image(mk_logo(url6), y = 5.5, scale = scale_fac) +
  draw_image(mk_logo(url7), y = 6.5, scale = scale_fac) +
  draw_image(mk_logo(url8), y = 7.5, scale = scale_fac) +
  draw_image(mk_logo(url9), y = 8.5, scale = scale_fac) +
  draw_image(mk_logo(url10), y = 9.5, scale = scale_fac)


# insert the image strip into the plot
ggdraw(insert_yaxis_grob(
  p, 
  pimage, 
  position = "left",
  width = unit(15, "mm")))
```

![](docs/holidayepisodes_tidy.png)

## **8.4 Direct labelling**

An Example using `directlabels` package [@directlabels-2] is at @fig-8-4-a

```{r}
#| label: fig-8-4-a
#| fig-cap: "Using directlabels and annotations to make reading the scatterplot easier, instead of a legend"
#| fig-width: 10

library(directlabels)

# Top 10 TV Series with most holiday season episodes
names_series <- holep |> 
  count(parent_primary_title, sort = TRUE) |> 
  filter(n > 10) |> 
  pull(parent_primary_title)

select_name = "Holiday Baking Championship"
n = 5

holep |> 
  filter(parent_primary_title %in% names_series[1:n]) |> 
  ggplot(
    aes(x = num_votes,
        y = average_rating,
        color = parent_primary_title)
  ) +
  # Background Highlighting of specific series
  geom_point(
    data = (holep |> filter(parent_primary_title == select_name)),
    size = 5,
    color = "lightgrey"
  ) +
  
  # Plotting all the points
  geom_point() +
  
  # Text Annotation Arrow
  annotate(
    geom = "curve",
    x = (holep |> 
           filter(parent_primary_title == select_name) |> 
           arrange(average_rating) |> 
           slice_head(n = 1) |> 
           pull(num_votes)),
    y = (holep |> 
           filter(parent_primary_title == select_name) |> 
           arrange(average_rating) |> 
           slice_head(n = 1) |> 
           pull(average_rating)),
    xend = 80, yend = 4,
    arrow = arrow(length = unit(2, "mm")),
    col = "darkgrey"
  ) +
  
  # Text Annotation
  annotate(
    geom = "label",
    x = 80, y = 4,
    hjust = 0,
    vjust = 0.5,
    label = paste0("TV Episodes of\n", select_name),
    fill = "grey",
    fontface = "italic",
    label_padding = unit(15, "mm"),
    label_size = unit(0, "mm")
  ) +
  
  # Labels and Titles
  labs(
    x = "Number of Votes for the episode on IMDb",
    y = "Average Rating of the Episode",
    title = paste0("Ratings and Votes for episodes of ", select_name),
    subtitle = paste0("Comparison with other series in the top ", n, " TV Series by number of holiday episodes")
  ) +
  scale_x_continuous(trans = "log10") +
  scale_color_brewer(palette = "Set1") + 
  cowplot::theme_half_open() +
  theme(
    axis.title = element_text(hjust = 1),
    legend.position = "none",
    axis.line = element_line(arrow = arrow(length = unit(3, "mm")))
  ) +
  
  # Using directlabels
  directlabels::geom_dl(
    aes(label = parent_primary_title),
    method = "smart.grid"
  )
```

Another example, which uses `geom_mark_ellipse()` of `ggforce` package [@ggforce-2] to focus on specific groups within a scatter-plot. The @fig-8-4-b shows this.

```{r}
#| label: fig-8-4-b
#| fig-cap: "Using ellipses to highlight areas of specific groups in a scatterplot"
#| fig-width: 10

names_highlight = c("Holiday Baking Championship",
                    "Thomas & Friends")

holep |> 
  filter(parent_primary_title %in% names_series[1:n]) |> 
  ggplot(
    aes(x = num_votes,
        y = average_rating,
        color = parent_primary_title)
  ) +
  # Background Highlighting of specific series
  geom_point(
    data = (holep |> filter(parent_primary_title %in% names_highlight)),
    size = 5,
    color = "lightgrey"
  ) +
  
  # Plotting all the points
  geom_point() +
  
 
 # Labels and Titles
  labs(
    x = "Number of Votes for the episode on IMDb",
    y = "Average Rating of the Episode",
    title = paste0("Ratings and Votes for TV Series with most holiday episodes"),
    subtitle = paste0("Highlighting the TV Series: ", paste0(names_highlight, collapse = ", "))
  ) +
  ggforce::geom_mark_ellipse(
    data = (holep |> filter(parent_primary_title %in% names_highlight)),
    aes(label = parent_primary_title,
        group = parent_primary_title,
        fill = parent_primary_title),
    linetype = 2,
    alpha = 0.2,
    label.margin = margin(0,0,0,0),
    con.linetype = 2,
    label.fill = "lightgrey"
  ) +
  scale_x_continuous(trans = "log10") +
  scale_color_brewer(palette = "Set1") + 
  scale_fill_brewer(palette = "Set1") +
  cowplot::theme_half_open() +
  theme(
    axis.title = element_text(hjust = 1),
    legend.position = "none",
    axis.line = element_line(arrow = arrow(length = unit(3, "mm")))
  )
```

## **8.5 Annotation across facets**

Similarly, using `gghighlight` package [@gghighlight-2], we can annotate different facets in one go, as shown in @fig-8-5.

```{r}
#| label: fig-8-5
#| fig-cap: "Annotating different facets by using gghighlight"
#| fig-width: 10


holep |> 
  filter(parent_primary_title %in% names_series[1:4]) |> 
  ggplot(
    aes(x = num_votes,
        y = average_rating,
        color = parent_primary_title)
  ) +
  
  # Plotting all the points
  geom_point(size = 2) +
  
  # Faceting by TV Series
  facet_wrap(~ parent_primary_title) +
  
  # gghighlight to annotate
  gghighlight::gghighlight() +
 
  # Labels and Titles
  labs(
    x = "Number of Votes for the episode on IMDb",
    y = "Average Rating of the Episode",
    title = paste0("Ratings and Votes for 4 TV Series with most holiday episodes"),
    subtitle = "Highlighting the TV Series in each panel."
  ) +
  scale_x_continuous(trans = "log10") +
  scale_color_brewer(palette = "Set1") + 
  scale_fill_brewer(palette = "Set1") +
  cowplot::theme_half_open() +
  theme(
    axis.title = element_text(hjust = 1),
    legend.position = "none",
    axis.line = element_line(arrow = arrow(length = unit(3, "mm"))),
    strip.background = element_rect(fill = "white")
  )
```